Since the COVID-19 pandemic, wearing masks has become a common practice worldwide, further compounded by the growing issue of air pollution. As masks have become an essential part of daily life, they pose a significant challenge to facial recognition systems, which struggle to accurately identify individuals when key facial features are obscured. This study focuses on improving the performance of existing masked facial recognition systems to better adapt to these real-world conditions. This work builds on an existing framework for masked face detection, proposing several optimizations to enhance recognition accuracy. The approach integrates Mediapipe for reliable face detection, combined with ResNet50 for feature extraction and a novel Feature Concatenation technique that merges multiple facial feature vectors to create more robust representations. To improve model adaptability to masked faces, we employ data augmentation techniques that simulate various real-world conditions, such as different mask types and lighting variations. Additionally, we performed ResNet50 fine-tuning on a specific masked dataset to increase robustness against partial face occlusions. Recognition is then carried out using a cosine similarity-based distance metric, ensuring both computational efficiency and high accuracy. Our optimizations resulted in a significant performance improvement, achieving a precision of 99.47% on the Celebrity dataset. These results demonstrate the potential to refine existing methodologies for better masked facial recognition, contributing to more reliable applications in security, healthcare, and other sectors.